Ontology Engineering approaches based on semi-automated curation of the primary literature Gully APC Burns, Tommy Ingulfsen, Donghui Feng and Ed Hovy Biomedical.

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Ontology Engineering approaches based on semi-automated curation of the primary literature Gully APC Burns, Tommy Ingulfsen, Donghui Feng and Ed Hovy Biomedical Knowledge Engineering Group, Information Sciences Institute, University of Southern California

Where’s all the knowledge? Image taken from U.S. Geological Survey Energy Resource Surveys Program The primary research literature... … is the end-product of all scientific research … forms the basis for human understanding of the subject... is written in natural language … is structured … is interpretable … is expensive … is terse

Precision and imprecision in biological representation Assay: define model system Experiment: perform measurements Conceptual model ‘Stress’, ‘energy balance’, ‘homeostasis’, ‘glucoprivation’ 2-deoxyglucose (2DG) administrated intravenously to rats, look for activation in ‘stress-responsive’ neurons MAP-K and pERK activate in neurons in PVH, BST and CEAl High-level concepts Independent variables Dependent variables Imprecise Precise

Partitioning the literature

The problem with knowledge: an over-abundance of data

Corpus Preparation for Natural Language Processing The Journal of Comparative Neurology is the foremost international journal for neuroanatomy. We downloaded ~12,000 PDFs in total from We preprocessed papers with consistent formatting from vol ( ) providing a corpus of 9,474 PDF files. This corpus contains 99,094,318 words

Active Learning / Information Extraction Methodology

The logical structure of a tract- tracing experiment Tracer Chemical [1] Injection Site [1]  Location brain structure topography side Labeled region [1...*]  Location brain structure topography ipsi-contra relative to injection site?  Label type  Label density ‘anterograde’ ‘retrograde’

Annotated XML Example from Albanese & Minciacchi, 1983, JCN 216: expt. label delineation injection labeling description

Recall, Precision and F-Score

Field Labeling Results – overall label level System FeaturesPrecisionRecallF-Score Baseline Lexicon Lexicon + Surface Words Lexicon + Surface Words + Window Words Lexicon + Surface + Window Words + Dependency features Preliminary data from a training set of 14 documents + testing on 16 documents

Field Labeling Results- Confusion Matrices

Generalizing the methodology: ‘Histology’ [from Gonzalo-Ruiz et al 1992, JCN 321: ]

The logical structure of a tract- tracing experiment Tracer Chemical [1] Injection Site [1]  Location brain structure topography side Labeled region [1...*]  Location brain structure topography ipsi-contra relative to injection site?  Label type  Label density ‘anterograde’ ‘retrograde’

Time and effort Current performance achieved by annotating 40 documents Each document contains 97 sentences (in results section) on average Annotation rate  ~ 40 Sent/hr (no support)  ~115 Sent/hr (after 20 documents) Time taken to annotate document to train system to perform at this standard  ~65 hours with no support  Estimate ~2 months for a 50% RA (20 hours / week)

Can we discover the schema from the text? Given a large review or a grant proposal specific to a single laboratory Annotate independent and dependent variables in papers. Can we learn and extract these patterns?

An example from current set of annotations 10 independent variables: age species sex weight agonist/antagonist combinations (9) primary antibody preparation protocol brain region 1 dependent variable: signal density

Acknowledgements Funding  Information Sciences Institute, seed funding *  National Library of Medicine (RO1-LM07061) *  NSF (LONI MAP project)  HBP (USCBP) Neuroscience consultants  Alan Watts *  Larry Swanson *  Arshad Khan *  Rick Thompson *  Joel Hahn *  Lori Gorton *  Kim Rapp * Computer Scientists  Eduard Hovy *  Donghui Feng *  Patrick Pantel * Developers  Tommy Ingulfsen *  Wei-Cheng Cheng